fix bug

recommend-model-produce/src/main/python/models/dssm/config_ps.yaml

@@ -25,7 +25,7 @@ runner:
 
   use_gpu: False
   epochs: 10
-  print_interval: 10
+  print_interval: 1
   
   test_data_dir: "data/test"
   infer_reader_path: "bq_reader_infer"  # importlib format

recommend-model-produce/src/main/python/models/dssm/net.py

@@ -100,11 +100,11 @@ class DSSMLayer(nn.Layer):
 
     def forward(self, left_features, right_features):
         # 获取两个视频的特征表示
-        paddle.static.Print(left_features, message="lqc left model input shape:")
-        paddle.static.Print(right_features, message="lqc right model input shape:")        
+        #paddle.static.Print(left_features, message="lqc left model input shape:")
+        #paddle.static.Print(right_features, message="lqc right model input shape:")        
         left_vec, right_vec = self.get_vectors(left_features, right_features)
-        paddle.static.Print(left_vec, message="lqc left model output shape:")
-        paddle.static.Print(right_vec, message="lqc right model output shape:")
+        #paddle.static.Print(left_vec, message="lqc left model output shape:")
+        #paddle.static.Print(right_vec, message="lqc right model output shape:")
         # 计算相似度
         sim_score = F.cosine_similarity(
             left_vec, 
@@ -124,22 +124,22 @@ class DSSMLayer(nn.Layer):
         left_vec = paddle.reshape(left_embedded, [-1, self.feature_num * self.embedding_dim])
               
         
-        paddle.static.Print(left_vec, message=f"lqc lqc left_vec:")
+        #paddle.static.Print(left_vec, message=f"lqc lqc left_vec:")
         
         
         
         for i, layer in enumerate(self._left_tower):
             left_vec = layer(left_vec)
-            paddle.static.Print(left_vec, message=f"After left layer {i}:")
+            #paddle.static.Print(left_vec, message=f"After left layer {i}:")
         
         # 处理右视频特征
         right_embedded = self._process_features(right_features, self.right_embeddings)
         # right_vec = right_embedded
         right_vec = paddle.reshape(right_embedded, [-1, self.feature_num * self.embedding_dim])  
-        paddle.static.Print(right_vec, message=f"lqc lqc left_vec:")
+        #paddle.static.Print(right_vec, message=f"lqc lqc left_vec:")
         for layer in self._right_tower:
             right_vec = layer(right_vec)
-            paddle.static.Print(right_vec, message=f"After left layer {i}:")
+            #paddle.static.Print(right_vec, message=f"After left layer {i}:")
             
         # 确保输出是L2归一化的
         left_vec = F.normalize(left_vec, p=2, axis=1)